SEO
A Complete Guide To B2B Multitouch Attribution Models
Executives have always desired a “single source of truth” to measure marketing effectiveness and avoid wasted ad spending.
John Wanamaker famously said, “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.”
While today’s data-driven marketers have untold access to data and metrics, the question is still as valid today as it was in John’s day
Which marketing activities contribute to the bottom line – and which ones do not?
Each online platform – be it Instagram, Facebook ads, Google Ads, LinkedIn, or YouTube – wants you to spend more money with it.
But it is not always possible to gain the full picture of accurate marketing performance.
Thankfully for us marketers, multi-touch (or multi-funnel) attribution may be the solution.
A multi-touch attribution model (MTA) enables you to understand each touchpoint’s role in creating a new customer.
This shows you what to leverage to boost performance and hit growth targets.
That’s why it is so important to gain a complete picture since every touchpoint a customer has with your brand can influence their decision to convert.
Once you understand which touchpoints result in conversions, you can better allocate your budgets to similar touchpoints in the future and reduce funds from less effective ones.
First, How Does Multi-Touch Attribution Work?
Multi funnel attribution – also known as multi-touch attribution – is a way to measure conversions.
It considers every touchpoint in the customer marketing journey and gives tribute to each channel to show the value of each touchpoint.
The challenge most marketers face is which channel or touchpoint to credit and how much to credit each touchpoint for the conversion.
In the graph showing a customer journey above, should the Facebook ad (first touchpoint) get all the credit, the Google paid-search ad (last), or all of them?
We’ll take you through all the attribution models to better understand how you could shape your measurements.
What Is Multi-touch Attribution?
Multi-touch attribution models refer to those that evaluate and weigh the impact of several touchpoints, not all attribution models.
As a result, they only consider the first or last touchpoint encountered before a conversion rather than every touchpoint encountered throughout the sales cycle.
What Multi Funnel Attribution Is Not
An easy way to understand multi-touch attribution is to compare it with other attribution models.
It Is Not First-Touch Attribution
Under the first-touch attribution model, the initial marketing touchpoint of a campaign before a closed sale is given full credit for the sale. This is where awareness marketing campaigns get credit for triggering a sale at the top of the funnel.
This may be useful for niche situations, but it gives no attention to the middle or bottom of the funnel activities.
However, this can be useful as companies prepare themselves for when there are no 3rd party cookies in the future, and other metrics – such as the first point of contact – need to be tracked.
It Is Not Last-Touch Attribution
In this model, the final touchpoint that has been interacted with before a closed lead is given full sales credit.
Last-touch attribution seems to be used more frequently than first-touch.
This attribution method is primarily concerned with the end stage of the customer journey and doesn’t focus on top or mid-funnel activities.
Why Adopt A Multi-touch Attribution Approach For B2B Marketing
Since you sell to companies, not individuals, you need more of an account-based attribution model rather than focused purely on the individual.
While some B2B transactions are conducted as B2C transactions, most B2B attributions need you to consider the many stakeholders in your buying journey to the account level.
These stakeholders are responsible for determining whether the company will buy from you.
Companies, Not Individuals
It is easy to lose sight that B2B means that you sell products or services to companies, not individuals.
The buying process includes users, decision-makers, stakeholders, and other advisers, but ultimately a company has to decide whether to pay another company for its solutions.
However, account-level or not, their journey will mirror the consumer’s.
Your brand and product are still going to be researched and engaged across all your different channels, including:
- Website.
- Live chat.
- Email.
- Phone calls.
- Review sites.
- Your product’s free trial.
- Social media.
Organized Data, Better Strategies
All of this data is difficult to organize, understand, and build a useful attribution model without structured thinking.
That is why a multi-touch attribution model, which provides a more granular, human-centric view of a campaign than traditional methods such as media mix modeling, is becoming more important for marketers.
In addition to providing visibility into the success of touchpoints across the customer journey, multi-touch attribution offers many other benefits.
It is crucial to utilize data-driven marketing to use the right channel to meet consumers at the right time, as consumers are becoming increasingly adept at avoiding marketing messages.
Multi-touch attribution makes this possible, which provides marketers with granular data to identify audiences across channels and determine their specific marketing goals.
Return On Investment
Multi-touch attribution models can help marketers improve the consumer experience and help them increase the return on their marketing expenditures by revealing where their money is being spent most and least effectively.
This supports a shorter, more effective sales cycle by presenting consumers with more impactful marketing messages.
Quick Overview Of The Types Of Multi-Touch Attribution Models
In multi-touch attribution, each touchpoint engages with the customer before conversion – the difference is the amount of credit attributed to each touchpoint. These models can either be adopted as is or modified to create custom models.
Linear Multi-touch Attribution Model
When you use a linear attribution model, each touchpoint in the buyer’s journey receives the same amount of credit for driving the sale. While this type of attribution considers all touchpoints in the buyer journey, it weighs each equally.
Although linear attribution improves first or last touch attribution, it still leaves a lot to be desired as all touchpoints don’t equally impact consumers.
U-shaped Multi-Touch Attribution Model
Based on the U-shaped multi-touch attribution strategy, 40% of value is attributed to the initial and last contact, and 20% goes to the subsequent touchpoints.
It provides your team with a clear picture of where the customer’s journey has begun, and where the journey ends.
Because this attribution model considers that not all touchpoints are equal, it is more reflective of how marketers value touchpoints intrinsically.
This is because it gives gravitas to the starting and ending campaigns.
Even so, it doesn’t meet all of the customer’s journey requirements due to its simplistic viewpoint.
Time Decay Multi-touch Attribution
This model gives a larger share of credit to customer touchpoints closer to conversion.
Although it gives some credit to touchpoints in the top and middle of the funnel, this article focuses primarily on touchpoints at the end of the marketing funnel.
The time decay model emphasizes touchpoints that directly lead to conversions and ignores awareness-based touchpoints.
While conversions are critical to ensuring your business is profitable, downplaying the first touchpoint is not perfect for all marketing teams.
The W-Shaped Multi-Touch Attribution Model
The W-shaped model is responsible for assigning credit of 30% at the first touch, mid-way (lead creation), and final (conversion) touchpoints.
The remaining 10% is equally split between additional engagements.
This model is ideal when there is a clearly defined “opportunity creation” stage in the journey.
And, while this is a massive improvement on the “one or none” approach, it is not always the best model for marketing teams to accurately attribute conversions.
Full Path Multi-Touch Attribution Model
This model – commonly used in B2B spaces – is quite detailed and complex.
Similar to the W-shaped multi-touch attribution, it has the addition of the lead creation touchpoint.
This notes the moment that a marketing lead becomes a qualified lead.
Here, 22.5% of the credit is attributed to the first touchpoint and lead generation, opportunity creation, and sale touchpoint, with the remaining 10% spread among the leftover touchpoints.
It is useful because it gives a granular view of the customer’s journey from start to finish.
So granular, in fact, that it may not be the best choice for B2C companies or those with low-involvement purchases.
Tailored Multi-Touch Attribution Model
The company itself designs this model.
It allows marketers to base value per touchpoint against their own parameters.
This is ideal for those who want to get the most from multiple models.
It can be difficult to put all the benefits of bespoke attribution together.
You may need to invest in software and attribution modeling experts to tailor your attribution strategy properly.
How To Deploy Multi-funnel Attribution For B2B
This can be a daunting task, but here are the steps we use to roll out a multi-touch attribution model.
Identify The Models And KPIs
Choose the attribution models that suit your organization best. Consider the length of the sales cycle, the types of campaigns, and the level of detail required. Then, identify the key metrics to measure success or failure.
Bring The Team Onboard
Your in-house team may need to bring on some external marketing analysts and strategists to get this job done. Internal finance and creative teams will also need to understand how data will drive campaigns going forward.
Setup Tracking
Start here:
- Collect the data. Who is visiting your site, how did they get there, and did they convert?
- JavaScript, where you add code to your website’s pages to understand who is interacting with your site and how. This includes call tracking such as page views, user activity, user identity, and traffic source.
- UTMs are custom URLs that allow you to track campaign-specific clicks and actions. UTMs can be integrated with the JavaScript calls to get a clearer, more accurate image of your user. In addition to maximizing insights, it enables B2B performance marketers to optimize spending, campaigns, and ROAS by stamping UTM attributes at the account level for website visitors. When used in conjunction with account-based retargeting ads, this has the potential to leverage massive growth.
- APIs can be integrated with your CRM system, external advertising vendors, and third-party software that have unique ways of identifying your users.
- Combine the data. To turn this raw data into useful insights, you need a place to store it, such as a central, secure data warehouse.
- Visualize the data. It is important to transform this data into graphs and charts that non-analytic stakeholders will find easier to understand. There are many vendors available who can do this for you.
- Invest in analytics software. If your attribution models are complex, it is best to implement analytics software that is advanced enough to work with your models. This will standardize and correlate the spans of raw data into reports that offer insights. Ideally, it will highlight consumer motivation, such as strong brand equity, compelling campaign creatives, etc.
- Apply insights and remodeling. Once you have collected and cleaned the data, use it to try and predict what might come based on past observations. Those insights can be translated into campaign improvements right away.
- Optimize and test. Tracking and testing are never done. Embrace a culture of continually evaluating your MTA data and testing campaigns to improve results.
- A/B testing: Tools like Google Optimize, Optimizely, or your strategic marketing partner make it easy to change campaigns to see what audiences prefer.
- Server-side testing: Growing in popularity for channels like SEO if other methods aren’t working.
- Geo experiments: For channels that cannot be A/B tested (such as TV), splitting campaigns by geographical region is useful to see the impact of the marketing on sales.
- Deprivation testing: Quite simply, switching the ad off and then on again to see its impact on sales and conversions.
Is MTA The Same As Multi-Channel Attribution?
Quite simply – no. Multi-channel attribution allocates credit according to channel (social advertising, paid search, organic SEO, etc.). It does not take into account specific touchpoints, messaging, or sequences.
While multi-touch attribution does factor in the channel, it is more granular in that it zooms in on each of the ads, their creatives, messaging, sequencing of interaction, and so on.
How Do We Know If We Need Multi-Touch Attribution?
It is best to apply MTA to campaigns that pivot on digital spending and that need to link an individual to a specific marketing event.
This could be email or online paid advertising that spans multiple channels and devices.
If your campaigns require this level of insight, then MTA is a good fit for you.
Conclusion
Multi-touch attribution allows B2B marketers to respond more rapidly to changes in their target audience and greater market.
The granular understanding they are given at an account level of which elements of their campaigns are working – and those that are not – means they can be flexible, agile, and competitive.
They have clarity into every touchpoint on the B2B customer journey, empowering marketing teams to make better data-backed decisions going forward.
Remember, B2B marketing attribution isn’t so much about budget as what marketing teams are doing.
Finding the right attribution model is imperative to success.
If yours is not supplementing your strategy with useful data, it will negatively impact your performance.
Every dot of data, every graph, and report should give you more insights into your ideal customer and their typical behavior.
As modern B2B marketers, we must have multiple weapons in our digital arsenal.
This will bring clarity to data chaos and give the organization an edge that will help them forge ahead with confidence.
More resources:
Featured Image: MaximP/Shutterstock
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SEO
How To Use ChatGPT For Keyword Research
Anyone not using ChatGPT for keyword research is missing a trick.
You can save time and understand an entire topic in seconds instead of hours.
In this article, I outline my most effective ChatGPT prompts for keyword research and teach you how I put them together so that you, too, can take, edit, and enhance them even further.
But before we jump into the prompts, I want to emphasize that you shouldn’t replace keyword research tools or disregard traditional keyword research methods.
ChatGPT can make mistakes. It can even create new keywords if you give it the right prompt. For example, I asked it to provide me with a unique keyword for the topic “SEO” that had never been searched before.
“Interstellar Internet SEO: Optimizing content for the theoretical concept of an interstellar internet, considering the challenges of space-time and interplanetary communication delays.”
Although I want to jump into my LinkedIn profile and update my title to “Interstellar Internet SEO Consultant,” unfortunately, no one has searched that (and they probably never will)!
You must not blindly rely on the data you get back from ChatGPT.
What you can rely on ChatGPT for is the topic ideation stage of keyword research and inspiration.
ChatGPT is a large language model trained with massive amounts of data to accurately predict what word will come next in a sentence. However, it does not know how to do keyword research yet.
Instead, think of ChatGPT as having an expert on any topic armed with the information if you ask it the right question.
In this guide, that is exactly what I aim to teach you how to do – the most essential prompts you need to know when performing topical keyword research.
Best ChatGPT Keyword Research Prompts
The following ChatGPT keyword research prompts can be used on any niche, even a topic to which you are brand new.
For this demonstration, let’s use the topic of “SEO” to demonstrate these prompts.
Generating Keyword Ideas Based On A Topic
What Are The {X} Most Popular Sub-topics Related To {Topic}?
The first prompt is to give you an idea of the niche.
As shown above, ChatGPT did a great job understanding and breaking down SEO into three pillars: on-page, off-page & technical.
The key to the following prompt is to take one of the topics ChatGPT has given and query the sub-topics.
What Are The {X} Most Popular Sub-topics Related To {Sub-topic}?
For this example, let’s query, “What are the most popular sub-topics related to keyword research?”
Having done keyword research for over 10 years, I would expect it to output information related to keyword research metrics, the types of keywords, and intent.
Let’s see.
Screenshot from ChatGPT 4, April 2024Again, right on the money.
To get the keywords you want without having ChatGPT describe each answer, use the prompt “list without description.”
Here is an example of that.
List Without Description The Top {X} Most Popular Keywords For The Topic Of {X}
You can even branch these keywords out further into their long-tail.
Example prompt:
List Without Description The Top {X} Most Popular Long-tail Keywords For The Topic “{X}”
Screenshot ChatGPT 4,April 2024List Without Description The Top Semantically Related Keywords And Entities For The Topic {X}
You can even ask ChatGPT what any topic’s semantically related keywords and entities are!
Screenshot ChatGPT 4, April 2024Tip: The Onion Method Of Prompting ChatGPT
When you are happy with a series of prompts, add them all to one prompt. For example, so far in this article, we have asked ChatGPT the following:
- What are the four most popular sub-topics related to SEO?
- What are the four most popular sub-topics related to keyword research
- List without description the top five most popular keywords for “keyword intent”?
- List without description the top five most popular long-tail keywords for the topic “keyword intent types”?
- List without description the top semantically related keywords and entities for the topic “types of keyword intent in SEO.”
Combine all five into one prompt by telling ChatGPT to perform a series of steps. Example:
“Perform the following steps in a consecutive order Step 1, Step 2, Step 3, Step 4, and Step 5”
Example:
“Perform the following steps in a consecutive order Step 1, Step 2, Step 3, Step 4 and Step 5. Step 1 – Generate an answer for the 3 most popular sub-topics related to {Topic}?. Step 2 – Generate 3 of the most popular sub-topics related to each answer. Step 3 – Take those answers and list without description their top 3 most popular keywords. Step 4 – For the answers given of their most popular keywords, provide 3 long-tail keywords. Step 5 – for each long-tail keyword offered in the response, a list without descriptions 3 of their top semantically related keywords and entities.”
Generating Keyword Ideas Based On A Question
Taking the steps approach from above, we can get ChatGPT to help streamline getting keyword ideas based on a question. For example, let’s ask, “What is SEO?”
“Perform the following steps in a consecutive order Step 1, Step 2, Step 3, and Step 4. Step 1 Generate 10 questions about “{Question}”?. Step 2 – Generate 5 more questions about “{Question}” that do not repeat the above. Step 3 – Generate 5 more questions about “{Question}” that do not repeat the above. Step 4 – Based on the above Steps 1,2,3 suggest a final list of questions avoiding duplicates or semantically similar questions.”
Screenshot ChatGPT 4, April 2024Generating Keyword Ideas Using ChatGPT Based On The Alphabet Soup Method
One of my favorite methods, manually, without even using a keyword research tool, is to generate keyword research ideas from Google autocomplete, going from A to Z.
-
Screenshot from Google autocomplete, April 2024
You can also do this using ChatGPT.
Example prompt:
“give me popular keywords that includes the keyword “SEO”, and the next letter of the word starts with a”
Screenshot from ChatGPT 4, April 2024Tip: Using the onion prompting method above, we can combine all this in one prompt.
“Give me five popular keywords that include “SEO” in the word, and the following letter starts with a. Once the answer has been done, move on to giving five more popular keywords that include “SEO” for each letter of the alphabet b to z.”
Generating Keyword Ideas Based On User Personas
When it comes to keyword research, understanding user personas is essential for understanding your target audience and keeping your keyword research focused and targeted. ChatGPT may help you get an initial understanding of customer personas.
Example prompt:
“For the topic of “{Topic}” list 10 keywords each for the different types of user personas”
Screenshot from ChatGPT 4, April 2024You could even go a step further and ask for questions based on those topics that those specific user personas may be searching for:
Screenshot ChatGPT 4, April 2024As well as get the keywords to target based on those questions:
“For each question listed above for each persona, list the keywords, as well as the long-tail keywords to target, and put them in a table”
Screenshot from ChatGPT 4, April 2024Generating Keyword Ideas Using ChatGPT Based On Searcher Intent And User Personas
Understanding the keywords your target persona may be searching is the first step to effective keyword research. The next step is to understand the search intent behind those keywords and which content format may work best.
For example, a business owner who is new to SEO or has just heard about it may be searching for “what is SEO.”
However, if they are further down the funnel and in the navigational stage, they may search for “top SEO firms.”
You can query ChatGPT to inspire you here based on any topic and your target user persona.
SEO Example:
“For the topic of “{Topic}” list 10 keywords each for the different types of searcher intent that a {Target Persona} would be searching for”
ChatGPT For Keyword Research Admin
Here is how you can best use ChatGPT for keyword research admin tasks.
Using ChatGPT As A Keyword Categorization Tool
One of the use cases for using ChatGPT is for keyword categorization.
In the past, I would have had to devise spreadsheet formulas to categorize keywords or even spend hours filtering and manually categorizing keywords.
ChatGPT can be a great companion for running a short version of this for you.
Let’s say you have done keyword research in a keyword research tool, have a list of keywords, and want to categorize them.
You could use the following prompt:
“Filter the below list of keywords into categories, target persona, searcher intent, search volume and add information to a six-column table: List of keywords – [LIST OF KEYWORDS], Keyword Search Volume [SEARCH VOLUMES] and Keyword Difficulties [KEYWORD DIFFICUTIES].”
-
Screenshot from ChatGPT, April 2024
Tip: Add keyword metrics from the keyword research tools, as using the search volumes that a ChatGPT prompt may give you will be wildly inaccurate at best.
Using ChatGPT For Keyword Clustering
Another of ChatGPT’s use cases for keyword research is to help you cluster. Many keywords have the same intent, and by grouping related keywords, you may find that one piece of content can often target multiple keywords at once.
However, be careful not to rely only on LLM data for clustering. What ChatGPT may cluster as a similar keyword, the SERP or the user may not agree with. But it is a good starting point.
The big downside of using ChatGPT for keyword clustering is actually the amount of keyword data you can cluster based on the memory limits.
So, you may find a keyword clustering tool or script that is better for large keyword clustering tasks. But for small amounts of keywords, ChatGPT is actually quite good.
A great use small keyword clustering use case using ChatGPT is for grouping People Also Ask (PAA) questions.
Use the following prompt to group keywords based on their semantic relationships. For example:
“Organize the following keywords into groups based on their semantic relationships, and give a short name to each group: [LIST OF PAA], create a two-column table where each keyword sits on its own row.
-
Screenshot from ChatGPT, April 2024
Using Chat GPT For Keyword Expansion By Patterns
One of my favorite methods of doing keyword research is pattern spotting.
Most seed keywords have a variable that can expand your target keywords.
Here are a few examples of patterns:
1. Question Patterns
(who, what, where, why, how, are, can, do, does, will)
“Generate [X] keywords for the topic “[Topic]” that contain any or all of the following “who, what, where, why, how, are, can, do, does, will”
Screenshot ChatGPT 4, April 20242. Comparison Patterns
Example:
“Generate 50 keywords for the topic “{Topic}” that contain any or all of the following “for, vs, alternative, best, top, review”
Screenshot ChatGPT 4, April 20243. Brand Patterns
Another one of my favorite modifiers is a keyword by brand.
We are probably all familiar with the most popular SEO brands; however, if you aren’t, you could ask your AI friend to do the heavy lifting.
Example prompt:
“For the top {Topic} brands what are the top “vs” keywords”
Screenshot ChatGPT 4, April 20244. Search Intent Patterns
One of the most common search intent patterns is “best.”
When someone is searching for a “best {topic}” keyword, they are generally searching for a comprehensive list or guide that highlights the top options, products, or services within that specific topic, along with their features, benefits, and potential drawbacks, to make an informed decision.
Example:
“For the topic of “[Topic]” what are the 20 top keywords that include “best”
Screenshot ChatGPT 4, April 2024Again, this guide to keyword research using ChatGPT has emphasized the ease of generating keyword research ideas by utilizing ChatGPT throughout the process.
Keyword Research Using ChatGPT Vs. Keyword Research Tools
Free Vs. Paid Keyword Research Tools
Like keyword research tools, ChatGPT has free and paid options.
However, one of the most significant drawbacks of using ChatGPT for keyword research alone is the absence of SEO metrics to help you make smarter decisions.
To improve accuracy, you could take the results it gives you and verify them with your classic keyword research tool – or vice versa, as shown above, uploading accurate data into the tool and then prompting.
However, you must consider how long it takes to type and fine-tune your prompt to get your desired data versus using the filters within popular keyword research tools.
For example, if we use a popular keyword research tool using filters, you could have all of the “best” queries with all of their SEO metrics:
-
Screenshot from Ahrefs Keyword Explorer, March 2024
And unlike ChatGPT, generally, there is no token limit; you can extract several hundred, if not thousands, of keywords at a time.
As I have mentioned multiple times throughout this piece, you cannot blindly trust the data or SEO metrics it may attempt to provide you with.
The key is to validate the keyword research with a keyword research tool.
ChatGPT For International SEO Keyword Research
ChatGPT can be a terrific multilingual keyword research assistant.
For example, if you wanted to research keywords in a foreign language such as French. You could ask ChatGPT to translate your English keywords;
Screenshot ChatGPT 4, Apil 2024- The key is to take the data above and paste it into a popular keyword research tool to verify.
- As you can see below, many of the keyword translations for the English keywords do not have any search volume for direct translations in French.
Screenshot from Ahrefs Keyword Explorer, April 2024But don’t worry, there is a workaround: If you have access to a competitor keyword research tool, you can see what webpage is ranking for that query – and then identify the top keyword for that page based on the ChatGPT translated keywords that do have search volume.
-
-
Screenshot from Ahrefs Keyword Explorer, April 2024
Or, if you don’t have access to a paid keyword research tool, you could always take the top-performing result, extract the page copy, and then ask ChatGPT what the primary keyword for the page is.
Key Takeaway
-
ChatGPT can be an expert on any topic and an invaluable keyword research tool. However, it is another tool to add to your toolbox when doing keyword research; it does not replace traditional keyword research tools.
As shown throughout this tutorial, from making up keywords at the beginning to inaccuracies around data and translations, ChatGPT can make mistakes when used for keyword research.
You cannot blindly trust the data you get back from ChatGPT.
However, it can offer a shortcut to understanding any topic for which you need to do keyword research and, as a result, save you countless hours.
But the key is how you prompt.
The prompts I shared with you above will help you understand a topic in minutes instead of hours and allow you to better seed keywords using keyword research tools.
It can even replace mundane keyword clustering tasks that you used to do with formulas in spreadsheets or generate ideas based on keywords you give it.
Paired with traditional keyword research tools, ChatGPT for keyword research can be a powerful tool in your arsenal.
More resources:
Featured Image: Tatiana Shepeleva/Shutterstock
SEO
OpenAI Expected to Integrate Real-Time Data In ChatGPT
Sam Altman, CEO of OpenAI, dispelled rumors that a new search engine would be announced on Monday, May 13. Recent deals have raised the expectation that OpenAI will announce the integration of real-time content from English, Spanish, and French publications into ChatGPT, complete with links to the original sources.
OpenAI Search Is Not Happening
Many competing search engines have tried and failed to challenge Google as the leading search engine. A new wave of hybrid generative AI search engines is currently trying to knock Google from the top spot with arguably very little success.
Sam Altman is on record saying that creating a search engine to compete against Google is not a viable approach. He suggested that technological disruption was the way to replace Google by changing the search paradigm altogether. The speculation that Altman is going to announce a me-too search engine on Monday never made sense given his recent history of dismissing the concept as a non-starter.
So perhaps it’s not a surprise that he recently ended the speculation by explicitly saying that he will not be announcing a search engine on Monday.
He tweeted:
“not gpt-5, not a search engine, but we’ve been hard at work on some new stuff we think people will love! feels like magic to me.”
“New Stuff” May Be Iterative Improvement
It’s quite likely that what’s going to be announced is iterative which means it improves ChatGPT but not replaces it. This fits into how Altman recently expressed his approach with ChatGPT.
He remarked:
“And it does kind of suck to ship a product that you’re embarrassed about, but it’s much better than the alternative. And in this case in particular, where I think we really owe it to society to deploy iteratively.
There could totally be things in the future that would change where we think iterative deployment isn’t such a good strategy, but it does feel like the current best approach that we have and I think we’ve gained a lot from from doing this and… hopefully the larger world has gained something too.”
Improving ChatGPT iteratively is Sam Altman’s preference and recent clues point to what those changes may be.
Recent Deals Contain Clues
OpenAI has been making deals with news media and User Generated Content publishers since December 2023. Mainstream media has reported these deals as being about licensing content for training large language models. But they overlooked a a key detail that we reported on last month which is that these deals give OpenAI access to real-time information that they stated will be used to give attribution to that real-time data in the form of links.
That means that ChatGPT users will gain the ability to access real-time news and to use that information creatively within ChatGPT.
Dotdash Meredith Deal
Dotdash Meredith (DDM) is the publisher of big brand publications such as Better Homes & Gardens, FOOD & WINE, InStyle, Investopedia, and People magazine. The deal that was announced goes way beyond using the content as training data. The deal is explicitly about surfacing the Dotdash Meredith content itself in ChatGPT.
The announcement stated:
“As part of the agreement, OpenAI will display content and links attributed to DDM in relevant ChatGPT responses. …This deal is a testament to the great work OpenAI is doing on both fronts to partner with creators and publishers and ensure a healthy Internet for the future.
Over 200 million Americans each month trust our content to help them make decisions, solve problems, find inspiration, and live fuller lives. This partnership delivers the best, most relevant content right to the heart of ChatGPT.”
A statement from OpenAI gives credibility to the speculation that OpenAI intends to directly show licensed third-party content as part of ChatGPT answers.
OpenAI explained:
“We’re thrilled to partner with Dotdash Meredith to bring its trusted brands to ChatGPT and to explore new approaches in advancing the publishing and marketing industries.”
Something that DDM also gets out of this deal is that OpenAI will enhance DDM’s in-house ad targeting in order show more tightly focused contextual advertising.
Le Monde And Prisa Media Deals
In March 2024 OpenAI announced a deal with two global media companies, Le Monde and Prisa Media. Le Monde is a French news publication and Prisa Media is a Spanish language multimedia company. The interesting aspects of these two deals is that it gives OpenAI access to real-time data in French and Spanish.
Prisa Media is a global Spanish language media company based in Madrid, Spain that is comprised of magazines, newspapers, podcasts, radio stations, and television networks. It’s reach extends from Spain to America. American media companies include publications in the United States, Argentina, Bolivia, Chile, Colombia, Costa Rica, Ecuador, Mexico, and Panama. That is a massive amount of real-time information in addition to a massive audience of millions.
OpenAI explicitly announced that the purpose of this deal was to bring this content directly to ChatGPT users.
The announcement explained:
“We are continually making improvements to ChatGPT and are supporting the essential role of the news industry in delivering real-time, authoritative information to users. …Our partnerships will enable ChatGPT users to engage with Le Monde and Prisa Media’s high-quality content on recent events in ChatGPT, and their content will also contribute to the training of our models.”
That deal is not just about training data. It’s about bringing current events data to ChatGPT users.
The announcement elaborated in more detail:
“…our goal is to enable ChatGPT users around the world to connect with the news in new ways that are interactive and insightful.”
As noted in our April 30th article that revealed that OpenAI will show links in ChatGPT, OpenAI intends to show third party content with links to that content.
OpenAI commented on the purpose of the Le Monde and Prisa Media partnership:
“Over the coming months, ChatGPT users will be able to interact with relevant news content from these publishers through select summaries with attribution and enhanced links to the original articles, giving users the ability to access additional information or related articles from their news sites.”
There are additional deals with other groups like The Financial Times which also stress that this deal will result in a new ChatGPT feature that will allow users to interact with real-time news and current events .
OpenAI’s Monday May 13 Announcement
There are many clues that the announcement on Monday will be that ChatGPT users will gain the ability to interact with content about current events. This fits into the terms of recent deals with news media organizations. There may be other features announced as well but this part is something that there are many clues pointing to.
Watch Altman’s interview at Stanford University
Featured Image by Shutterstock/photosince
SEO
Google’s Strategies For Dealing With Content Decay
In the latest episode of the Search Off The Record podcast, Google Search Relations team members John Mueller and Lizzi Sassman did a deep dive into dealing with “content decay” on websites.
Outdated content is a natural issue all sites face over time, and Google has outlined strategies beyond just deleting old pages.
While removing stale content is sometimes necessary, Google recommends taking an intentional, format-specific approach to tackling content decay.
Archiving vs. Transitional Guides
Google advises against immediately removing content that becomes obsolete, like materials referencing discontinued products or services.
Removing content too soon could confuse readers and lead to a poor experience, Sassman explains:
“So, if I’m trying to find out like what happened, I almost need that first thing to know. Like, “What happened to you?” And, otherwise, it feels almost like an error. Like, “Did I click a wrong link or they redirect to the wrong thing?””
Sassman says you can avoid confusion by providing transitional “explainer” pages during deprecation periods.
A temporary transition guide informs readers of the outdated content while steering them toward updated resources.
Sassman continues:
“That could be like an intermediary step where maybe you don’t do that forever, but you do it during the transition period where, for like six months, you have them go funnel them to the explanation, and then after that, all right, call it a day. Like enough people know about it. Enough time has passed. We can just redirect right to the thing and people aren’t as confused anymore.”
When To Update Vs. When To Write New Content
For reference guides and content that provide authoritative overviews, Google suggests updating information to maintain accuracy and relevance.
However, for archival purposes, major updates may warrant creating a new piece instead of editing the original.
Sassman explains:
“I still want to retain the original piece of content as it was, in case we need to look back or refer to it, and to change it or rehabilitate it into a new thing would almost be worth republishing as a new blog post if we had that much additional things to say about it.”
Remove Potentially Harmful Content
Google recommends removing pages in cases where the outdated information is potentially harmful.
Sassman says she arrived at this conclusion when deciding what to do with a guide involving obsolete structured data:
“I think something that we deleted recently was the “How to Structure Data” documentation page, which I thought we should just get rid of it… it almost felt like that’s going to be more confusing to leave it up for a period of time.
And actually it would be negative if people are still adding markup, thinking they’re going to get something. So what we ended up doing was just delete the page and redirect to the changelog entry so that, if people clicked “How To Structure Data” still, if there was a link somewhere, they could still find out what happened to that feature.”
Internal Auditing Processes
To keep your content current, Google advises implementing a system for auditing aging content and flagging it for review.
Sassman says she sets automated alerts for pages that haven’t been checked in set periods:
“Oh, so we have a little robot to come and remind us, “Hey, you should come investigate this documentation page. It’s been x amount of time. Please come and look at it again to make sure that all of your links are still up to date, that it’s still fresh.””
Context Is Key
Google’s tips for dealing with content decay center around understanding the context of outdated materials.
You want to prevent visitors from stumbling across obsolete pages without clarity.
Additional Google-recommended tactics include:
- Prominent banners or notices clarifying a page’s dated nature
- Listing original publish dates
- Providing inline annotations explaining how older references or screenshots may be obsolete
How This Can Help You
Following Google’s recommendations for tackling content decay can benefit you in several ways:
- Improved user experience: By providing clear explanations, transition guides, and redirects, you can ensure that visitors don’t encounter confusing or broken pages.
- Maintained trust and credibility: Removing potentially harmful or inaccurate content and keeping your information up-to-date demonstrates your commitment to providing reliable and trustworthy resources.
- Better SEO: Regularly auditing and updating your pages can benefit your website’s search rankings and visibility.
- Archival purposes: By creating new content instead of editing older pieces, you can maintain a historical record of your website’s evolution.
- Streamlined content management: Implementing internal auditing processes makes it easier to identify and address outdated or problematic pages.
By proactively tackling content decay, you can keep your website a valuable resource, improve SEO, and maintain an organized content library.
Listen to the full episode of Google’s podcast below:
Featured Image: Stokkete/Shutterstock
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